Artificial Intelligence #memory#optimization
E-mem: Multi-Agent Framework for Episodic Memory Reconstruction Boosts LLM Reasoning Efficiency by 70%
Researchers propose E-mem, a multi-agent framework that reconstructs episodic context for LLM agent memory, inspired by biological engrams. It uses a hierarchical architecture with assistant agents maintaining uncompressed contexts and a master agent orchestrating planning, achieving 54% F1 on the LoCoMo benchmark, surpassing the state-of-the-art GAM by 7.75% with over 70% token cost reduction.
Jun 16, 2026 2 sources